• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.64-69
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• 1993

In this paper we present the performance bounds of the optimal FIR filter in continuous time systems with modeling uncertainty. The performance measure bounds are calculated from the estimation error covariance bounds of the optimal FIR filter and the suboptimal FIR filter. Performance error bounds range are expressed by the upper bounds on the estimation error covariance difference between the real and nominal values in case of the systems with noise uncertainty or model uncertainty. The performance bounds of the systems are derived on the assumption that the system uncertainty and the estimation error covariance are imperfectly known a priori. The estimation error bounds of the optimal FIR filter is compared with those of the Kalman filter via a numerical example applied to the estimation of the motion of an aircraft carrier at sea, which shows the former has better performances than the latter.

• 한국전산유체공학회:학술대회논문집
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• 2008.03a
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• pp.79-83
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• 2008

An approach to CFD code validation is developed that gives proper consideration to experimental and simulation uncertainties. The comparison errors include the difference between the data, simulation values and represents the combination of all errors. The uncertainties of modeling and numerical analysis in the CFD prediction were estimated by a Coleman's theory. In this paper, the numerical solutions are calculated by A-type standard uncertainty and Richardson extrapolation Method.

• 한국전산유체공학회:학술대회논문집
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• 2008.10a
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• pp.79-83
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• 2008

An approach to CFD code validation is developed that gives proper consideration to experimental and simulation uncertainties. The comparison errors include the difference between the data, simulation values and represents the combination of all errors. The uncertainties of modeling and numerical analysis in the CFD prediction were estimated by a Coleman's theory. In this paper, the numerical solutions are calculated by A-type standard uncertainty and Richardson extrapolation Method.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.251-257
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• 2007

For the modern aircraft, uncertainty has bee an important issue to its aeroelastic stability. Therefore, many researches have been conducted regarding this topic. The uncertainties in the aeroelastic system amy consist of the structural and aerodynamic uncertainty. In this paper, we suggest a parametric uncertainty modeling and conduct the aeroelastic stability analysis of a typical wing including the uncertainty.

• Fire Science and Engineering
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• v.26 no.2
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• pp.40-52
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• 2012

This paper presents the uncertainty analysis results of fire modeling input parameters for motor control center in switchgear room of nuclear power plants. FDS (Fire Dynamics simulator) 5.5 was used to simulate the fire scenario and Latin Hyper Cube Monte Carlo simulations were employed to generate random samples for FDS input parameters. The uncertainty analysis results of input parameters are compared with those of the model uncertainty analysis and sensitivity analysis approaches of NUREG-1934. The study results show that the input parameter uncertainty analysis approach may lead to more conservative results than the uncertainty analysis and sensitivity analysis methods of NUREG-1934.

• Earthquakes and Structures
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• v.12 no.2
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• pp.165-175
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• 2017

This paper studies soil properties uncertainty and its implementation in the seismic response evaluation of structures. For this, response sensitivity of two 4- and 12-story RC shear walls to the soil properties uncertainty by considering soil structure interaction (SSI) effects is investigated. Beam on Nonlinear Winkler Foundation (BNWF) model is used for shallow foundation modeling and the uncertainty of soil properties is expanded to the foundation stiffness and strength parameters variability. Monte Carlo (MC) simulation technique is employed for probabilistic evaluations. By investigating the probabilistic evaluation results it's observed that as the soil and foundation become stiffer, the soil uncertainty is found to be less important in influencing the response variability. On the other hand, the soil uncertainty becomes more important as the foundation-structure system is expected to experience nonlinear behavior to more sever degree. Since full This paper studies soil properties uncertainty and its implementation in the seismic response evaluation of structures. For this, response sensitivity of two 4- and 12-story RC shear walls to the soil properties uncertainty by considering soil structure interaction (SSI) effects is investigated. Beam on Nonlinear Winkler Foundation (BNWF) model is used for shallow foundation modeling and the uncertainty of soil properties is expanded to the foundation stiffness and strength parameters variability. Monte Carlo (MC) simulation technique is employed for probabilistic evaluations. By investigating the probabilistic evaluation results it's observed that as the soil and foundation become stiffer, the soil uncertainty is found to be less important in influencing the response variability. On the other hand, the soil uncertainty becomes more important as the foundation-structure system is expected to experience nonlinear behavior to more sever degree. Since full probabilistic analysis methods like MC commonly are very time consuming, the feasibility of simple approximate methods' application including First Order Second Moment (FOSM) method and ASCE41 proposed approach for the soil uncertainty considerations is investigated. By comparing the results of the approximate methods with the results obtained from MC, it's observed that the results of both FOSM and ASCE41 methods are in good agreement with the results of MC simulation technique and they show acceptable accuracy in predicting the response variability.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.10a
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• pp.70-73
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• 2001

The layup optimization by genetic algorithm (GA) for the interlaminar strength of laminated composites with free edge is presented. For the calculation of interlaminar stresses of composite laminates with free edges, extended Kantorovich method is applied. In the formulation of GA, repair strategy is adopted for the satisfaction of given constraints. In order to consider the bounded uncertainty of material properties, convex modeling is used. Results of GA optimization with scattered properties are compared with those of optimization with nominal properties. The GA combined with convex modeling can work as a practical tool for maximum interlaminar strength design of laminated composite structures, since uncertainties are always encountered in composite materials and the optimal results can be changed.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.976-979
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• 1996

Teleoperation is the extension of a person's sensing and manipulation capability to a remote location. Teleoperators generally can be modeled as linear transfer function indecently including modeling uncertainty. Modeling uncertainties can make the system unstable and its performance poor. Thus I'm studying about a design framework for a bilateral controller of teleoperator systems with modeling uncertainties. In this paper, a method based on the H$_{\infty}$-optimal control and .mu.-synthesis frameworks are introduced to design a controller for the teleoperator that achieves stability and performance in the presence of the modeling uncertainties..ties.inties.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.